Simulation of Routing Protocols for Emergency Vehicle Warning Application in VANETs

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1 POSTER 2018, PRAGUE MAY 10 1 Simulation of Routing Protocols for Emergency Vehicle Warning Application in VANETs Tibor PETROV Dept. of Multimedia and Information-Communication Technologies, University of Zilina, Univerzitna 8215/1, Zilina, Slovak Republic tibor.petrov@fel.uniza.sk Abstract. Vehicular ad hoc networks (VANETs) can present a great benefit for road transport safety, effficiency and environmental impact. One of the applications that can utilize the VANETs to increase the traffic safety is an Emergency Vehicle Warning Application. For the application to leverage its potential, it is vital to find appropriate forwarding scheme to allow reliable and timely communication. In this paper, computer modelling using realistic VANET simulation stack is used to evaluate the end-to-end delay characteristics of common ad hoc routing protocols. After the evaluation of protocols' characteristics, we aim to propose the most suitable forwarding mechanism to support the Emergency Vehicle Warning Application. Keywords VANET, simulation, routing protocols, vehicular application. 1. Introduction Vehicular Ad hoc Networks (VANETs) are one of the key technologies of Intelligent Transportation Systems (ITS). Based on a communication between radio transceiver equipped vehicles and intelligent roadside infrastructure, they can provide critical information for both drivers and autonomous vehicles. These data can be used to prevent vehicle collisions, increase driver s awareness of road and traffic conditions, or plan the route to the destination in a most environmentally friendly manner. The communication between the network nodes can be carried out simply by a broadcasting or multicasting. In some cases, however, the targeting of the message to a specific host can be beneficial for the ease of the information interpretation. Let s assume an application, which provides a warning information about approaching emergency vehicle to ensure a fluent emergency vehicle transit. This information is relevant mostly to the vehicles in front of the emergency vehicle which are travelling the same route. More specifically, if the vehicles are on a road with multiple driving lanes, the information can be targeted to specific vehicles and make them change their lane to the rightmost one in order to free the left lane/lanes for the emergency vehicle. To compare the performance of the broadcast and the unicast approaches in VANETs, a simulation model was built using OMNeT++ and SUMO simulators with Veins simulation framework. 2. Simulation Tools With the growing attention on vehicular networks, several tools for vehicular network simulations were introduced. Some of them are reviewed in [1]. Vehicular networks differ from the conventional ad hoc networks mainly by their high mobility. In order to make their simulation as realistic as possible, several computer modeling tools have to be used in conjunction. For our simulations following modeling stack was used: OMNeT++ discrete event network simulator Simulation of Urban Mobility (SUMO) traffic simulator Veins and Artery simulation frameworks custom built stochastic traffic flow generator for SUMO OMNeT++ network simulator is used to obtain the network performance parameters like end-to-end (E2E) delay, average number of hops for a specific protocol, as well as for implementing the whole communication stack. The SUMO traffic simulator provides the mobility for the nodes modeled in OMNeT++. SUMO uses realistic maps exported from the Openstreetmap project. From the map, road network is extracted and converted using the SUMO's netconvert tool to XML format readable by SUMO. The exported road network is then used as a base for modeling a vehicle traffic flows. Actual traffic flows are modeled based on data obtained by a traffic survey for the Traffic General Plan of the City of Žilina. By this approach, a highly realistic vehicle mobility traces are obtained. [2-5]

2 2 Simulation frameworks provide compatibility with IEEE WAVE (Veins) and ETSI ITS G5 (Artery) communication standards for vehicular networks. But one of their biggest advantages is the fact that they implement the Traffic Control Interface (TraCI) API. TraCI provides a real-time bi-directional coupling of the OMNeT++ and the SUMO via TCP socket. This way, the traffic simulator can be controlled from the network simulator and vice versa. This enables modeling of realistic simulation scerasios, for example to change the vehicle's trajectory based on a content of a message it received. [6] 3. Routing in VANETs Currently, there are five approaches to routing in VANET networks. These approaches can be seen in the Fig.1. of large-scale networks, or networks with high mobility, signalization packets can overload the network, consuming large portion of available bandwidth, which cannot be utilized to transmit useful data Reactive protocols To minimize the signalization traffic in the network, new family of protocols was developed. Reactive protocols do not maintain complete routing information to all of the available networks. When a node wants to communicate with a distant network, a route discovery process is performed by sending a Route Request (RREQ) packet. If the neighboring node knows the route to the destination, it generates the Route Reply (RREP) packet which is then sent back to the source node using Backward learning method. Otherwise, the neighboring node will rebroadcast the RREQ [8]. This approach can significantly reduce the energy consumption and signalization load, however, it adds a variable delay to the network Hybrid protocols Hybrid routing protocols are a combination of proactive and reactive protocols. They try to utilize advantages of both the families. An example of a hybrid protocol can be the Zone Routing Protocol (ZRP), which uses proactive routing in a zone defined by a certain number of hops. Outside of this zone it uses the reactive routing approach. Fig. 1. Routing in VANETs [7] 3.1 Ad-hoc Based Routing (Topology Based Routing) Some of the routing protocols developed for MANETs and other ad-hoc networks can be used in VANETs with some modifications [7]. This family of protocols can be divided into three groups according to the way in which they maintain their routing tables Proactive protocols Proactive protocols maintain full routing table with all of the routing information to all known networks. All networks are periodically monitored and updated and the node has immediate access to the routing information. Every node maintains its own, complete routing table. If the topology of the network is changed, the information about all of the changes is distributed to the whole network. This ensures that all the nodes have complete knowledge about the topology of the network. Advantages of the proactive routing approach include minimum delay when establishing the connection between nodes, possibility of real-time communication. The proactive protocols provide better quality of service (QoS), as routes are allways up-todate and immediately available so the end-to-end delay in such a network can be minimized. [8] However, in the case 3.2 Location Based Routing Location based routing protocols utilize the coordinates of individual nodes to optimize routing. To use these protocols, it is assumed that each node knows its precise geographical position obtained from a GPS receiver or other source. [9] Each node contains information about the source, destination and neighboring nodes Greedy Perimeter Stateless Routing (GPSR) The GPSR was introduced in 2000 by Karp and Kung [10]. The routing algorithm uses positions of neighboring nodes and the destination node to forward the packet from the source to the destination. It makes forwarding decisions based only on the information about neighboring nodes. The node forwards packets to the neighboring node which is geographically closest to the packet s destination. If the node cannot find any next hop that is closer to the destination than itself (often referred to as the local maximum problem), the algorithm recovers by routing along the perimeter of the region, using the right-hand rule for traversing the graph. [10] [11] One of the biggest disadvantages of the GPSR protocol when used in VANETs is that its performance drops significantly when there are obstacles present, rendering it not suitable to use in the urban environments. [11]

3 POSTER 2018, PRAGUE MAY Geographic Source Routing (GSR) The GSR protocol uses street map of the city and location of the source and the destination node to calculate the progression of the key nodes, through which the packet should be forwarded in order to reach its destination. The optimal path is calculated using the Dijkstra s shortest path algorithm. Between the junction nodes, packets are forwarded using greedy forwarding node chooses the next hop that is geographically closest to the next junction. When the junction of the path is reached, the greedy forwarding algorithm is applied again to reach the next key node. The process repeats until the packet finally reaches its destination Anchor-based Street and Traffic Aware Routing (A - STAR) This protocol was specifically designed for the intervehicular communication systems in an urban environment. Like the GSR protocol, it calculates the list of junctions a packet must traverse in order to reach the destination. To overcome the problem of highly fragile connections in a VANETs and issues caused by an uneven density of the nodes, the protocol uses actual traffic information including number of public transport lines and traffic density to calculate high-connectivity path to the destination. Using this approach, the packets are delivered using streets with higher density of vehicles, which ensures higher connectivity among nodes and less frequent connection breakdowns. [9] [12] [13] Streets of the map are weighted according to the traffic density, while assigned weight of the street is inversely proportional to the traffic density on the street. After the streets of the map are weighted, an anchor path is computed using Dijkstra s least-weight path algorithm. Packets then traverse the calculated anchors to reach their destination. [9] [12] [13] 3.3 Cluster Based Routing Cluster is a group of nodes that can directly communicate to each other without disconnection. An example of clustering can be seen in the Fig.2. To coordinate the communication, a cluster head is selected by the nodes according to the so called suitability value. A vehicle which has higher number of stable neighbors, closer speed to average speed of its stable neighbors and maintains closer distance to its stable neighbors has higher priority to become a cluster head. [14] The vehicles within the cluster can communicate directly. Communication between clusters is performed via cluster heads. [9] Position data received by the periodic messages (Cooperative Awareness Messages - CAMs) are used to build neighborhood relationships between vehicles in the cluster. To transmit periodic messages, vehicles use the control channel (CCH). To perform all intra-cluster communication tasks and to define the cluster radius, one service channel (SCH) is used. Fig. 2. An example of cluster-based topology [9] 3.4 Geocast Based Routing Geocast routing can be understood as a multicasting based on the knowledge of geographical location of the nodes. A single source node forwards data to the destination area which is called Zone of Relevance (ZOR). In order to prevent flooding of the geocast message a forwarding area called a Zone of Forwarding (ZOF) is used to confine the message forwarding until it reaches the ZOR. [14] 4. Frame Structure Frame structure used in simulations can be seen in Fig.3. At the MAC layer, the frame consists of header, payload and a trailer. At the physical layer, preamble and signal are added. Transmissions were carried out using the default 6 Mbit/s data rate according to [16]. The preamble field consists of 12 OFDM symbols with total duration of 32 μs. The signal field is one OFDM symbol with duration 8 μs [17]. Fig. 3. Frame structure used in simulations Frame duration can be calculated according to the equation (1). td= Pl DR + Lp + Ls+ Dp + Hc[s ] (1) where td is the frame duration in seconds, Pl is the packet length in bits, DR is used datarate in bits per second, Lp is preamble duration, Ls signal duration, Dp propagation delay and Hc is clock hold-on. Parameters can be found in the Table.1.

4 4 Tab. 1. Frame duration parameters. 5. Results As can be seen in figures 4 and 5, the end-to-end delay significantly increases with growing number of next hops. This increase seems to be more significant in the case of AODV protocol, however, the GPSR protocol is unable to find route to the destination at extremely low signal-tonoise ratio. Here the AODV protocol outperforms the GPSR as can be seen in figures 4 and Conclusion and Further Work End-to-End delay of two ad-hoc routing protocols was examined by a simulation of transmissions of a real VANET application in a realistic traffic scenario. From the simulation results we can conclude that it is very difficult to use a single routing protocol in various scenarios. One of the studied protocols (AODV) achieved much higher average end-to-end delay, but it also was able to support the communication ina case of very low signalto-noise ratios. The GPSR protocol, on the other hand, showed opposite behavior. Average end-to-end delay in this case was much (100x) lower, however, the protocol was unable to establish the route in complicated communication environment. In our next research, we would like to similarly examine the behavior of DYMO and ETSI GeoNet protocols. Based on these results we would like to propose the most suitable routing scheme for an emergency vehicle warning application for VANETs which is currently being developed. Fig. 4. End-to-End delay for AODV protocol. Acknowledgements This paper was supervised by prof. Ing. M. Dado, PhD., Faculty of Electrical Engineering, University of Žilina. Research described in the paper was supported by the ERAChair Eradiate - Enhancing Research and innovation dimension of the University of Žilina in intelligent transport systems, Grant agreement no: , Project 7thFP. Fig. 5. End-to-End delay for GPSR protocol. References [1] PETROV, T., DADO, M., AMBROSCH, K.E. Computer Modelling of Cooperative Intelligent Transportation Systems in Procedia Engineering, Volume 192, Elsevier, pp , 2017 [2] VARGA, A., HORNIG, R. An Overview of the OMNeT++ Simulation Environment in Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops, Article No.: 60, 2008 [3] SUMO User Documentation [online], [4] Veins: The open source vehicular network simulation framework [online], [5] Územný general dopravy Mesta Žilina [online],

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